Classification of audio signals using statistical features on time and wavelet transform domains

Lambrou, Tryphon and Kudumakis, P. and Speller, R. and Sandler, M. and Linney, A. (1998) Classification of audio signals using statistical features on time and wavelet transform domains. In: 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, 12 - 15 May 1998.

Full content URL: http://dx.doi.org/10.1109/ICASSP.1998.679665

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

This paper presents a study on musical signal classification, using wavelet transform analysis in conjunction with statistical pattern recognition techniques. A comparative evaluation between different wavelet analysis architectures in terms of their classification ability, as well as between different classifiers is carried out. We seek to establish which statistical measures clearly distinguish between the three different musical styles of rock, piano, and jazz. Our preliminary results suggest that the features collected by the adaptive splitting wavelet transform technique performed better compared to the other wavelet based techniques, achieving overall classification accuracy of 91.67, using either the Minimum Distance Classifier or the Least Squares Minimum Distance Classifier. Such a system can play a useful part in multimedia applications which require content based search, classification, and retrieval of audio signals, as defined in MPEG-7.

Additional Information:Conference Code: 48801
Keywords:Acoustic noise, Algorithms, Feature extraction, Statistical methods, Wavelet transforms, Minimum distance classifier, Musical signal classification, Acoustic signal processing
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
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ID Code:8680
Deposited On:01 Aug 2013 11:40

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